Constraint Handling Methods for Resource-Constrained Robotic Disassembly Line Balancing Problem
Constraint Handling Methods for Resource-Constrained Robotic Disassembly Line Balancing Problem
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DOI：
10.1088/1742-6596/1576/1/012039
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Yilin Fang;Hongliang Xu
中科院分区：
文献类型：
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作者：
Yilin Fang;Hongliang Xu
分析了一种资源受限的机器人拆卸生产线平衡问题(RCDLBP)，该问题与传统的DLBP问题有很大的不同，因为机器人在执行拆卸任务时需要消耗一定数量的资源。这些资源可能是一些稀缺的拆卸工具，也可能是支持机器人正常工作的油和电。提出了一种RCDLBP的数学模型，该模型考虑了额外的资源约束，同时最小化了机器人的使用数量和周期时间。基于所建立的数学模型和基于分解的多目标进化算法(MOEA/D)相结合的三种约束处理方法，在16个测试用例上进行了实验。所有测试实例的初始种群可行率在99.99%到0.34%之间。结果表明，随着初始种群可行率的逐渐降低，不同约束处理方法的搜索能力发生了很大的变化。
In this paper, we analysed a resource-constrained robotic disassembly line balancing problem (RCDLBP), which was significantly different from traditional DLBP for the robots will consume a certain amount units of resources when performing disassembly tasks. These resources could be some scarce disassembly tools or the oil and electricity to support the normal work of robots. A mathematical model of RCDLBP which considering an additional resource constraint and simultaneously minimizes the cycle time and the number of robots used was proposed in this research. Based on the mathematical model and three constraint handling methods which integrated with multi-objective evolutionary algorithm based on decomposition (MOEA/D), we performed experiments on 16 test cases. The initial population feasible ratio of all test instances ranges from 99.99% to 0.34%. Results show that as the initial population feasible ratio gradually decreases, the search capabilities of different constraint handling methods changed dramatically.